Ning Jiang

Ning Jiang (S’02–M’09) received the B.S. degree in electrical engineering from Xi’an Jiaotong University, Xi’an, China, in 1998, and the M.Sc. and Ph.D. degrees in engineering from the University of New Brunswick, Fredericton, NB, Canada, in 2004 and 2009, respectively.

He was a Research Assistant Professor with Aalborg University, Denmark from 2009 to 2010, a Marie Curie Fellow with the Strategic Technology Management, Otto Bock Healthcare GmbH, Germany, from 2010 to 2012, and a Research Scientist with the Department of Neurorehabilitation Engineering, University Medical Center Göttingen, Georg-August University, Göttingen, Germany, from 2012 to 2015. Since 2015, he has been an Assistant Professor with the Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada. His research interests include signal processing of electromyography, advanced prosthetic control, neuromuscular modeling, electroencephalogram processing, and brain–computer interfaces for neurorehabilitation. He is 77 currently an Associate Editor of the IEEE JOURNAL of BIOMEDICAL AND HEALTH INFORMATICS, and the Brain-Computer Interface, and a Guest Editor of Frontiers in Neuroscience.

Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this study, we hypothesize that a combination of these two signal modalities provides improvements in BCI performance with respect to using the two methods separately, and generate novel types of multi-class BCI systems.

The detection of voluntary motor intention from EEG has been applied to closed-loop brain–computer interfacing (BCI). The movement-related cortical potential (MRCP) is a low frequency component of the EEG signal, which represents movement intention, preparation, and execution. In this study, we aim at detecting MRCPs from single-trial EEG traces. For this purpose, we propose a detector based on a discriminant manifold learning method, called locality sensitive discriminant analysis (LSDA), and we test it in both online and offline experiments with executed and imagined movements.

In this study, we present a novel multi-class brain-computer interface (BCI) system for communication and control. In this system, the information processing is shared by the algorithm (computer) and the user (human). Specifically, an electro-tactile cycle was presented to the user, providing the choice (class) by delivering timely sensory input.

We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attentional changes by exploring the oscillatory dynamics induced by actual tactile sensation. Offline analysis showed that the proposed calibration method led to higher accuracies than the traditional calibration method based only on somatosensory attentional orientation (SAO) data.